In this work, we focus on fairness in clustering, i.e., the balance of members in cluster w.r.t protected attributes, as in educational activities, group assignments [1]} and student team achievement divisions [2]} are important tools in teaching and learning tasks to help students working together towards shared learning goals.
Better communication, higher-order thinking, conflict management are several examples of the advantages of group assignments [1]}.
Clustering algorithms are effective solutions for partitioning students into groups of similar instances [4]}, [5]}. Traditional algorithms, however, focus solely on the similarity objective and do not consider the fairness of the resulting clusters w.r.t. protected attributes like gender or race. However, studies indicate that students might learn better in diverse student groups, e.g., mixed-gender groups [6]}, [7]}.
Lately, fair-clustering solutions have been proposed, e.g., [8]}, which aim to discover clusters with a fair representation regarding some protected attributes.
